Abstract
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient's health, enabling more personalized and effective care. This literature review explores the methodologies, challenges, and opportunities associated with PCKGs, focusing on their role in integrating disparate healthcare data and enhancing patient care through a unified health perspective. In addition, this review also discusses the complexities of PCKG development, including ontology design, data integration techniques, knowledge extraction, and structured representation of knowledge. It highlights advanced techniques such as reasoning, semantic search, and inference mechanisms essential in constructing and evaluating PCKGs for actionable healthcare insights. We further explore the practical applications of PCKGs in personalized medicine, emphasizing their significance in improving disease prediction and formulating effective treatment plans. Overall, this review provides a foundational perspective on the current state-of-the-art and best practices of PCKGs, guiding future research and applications in this dynamic field.
Abstract (translated)
患者为中心的知识图（PCKGs）代表了医疗保健领域的一个重要转变，通过将患者的健康信息以整体和多维方式进行映射，实现个性化患者护理。PCKGs 整合了各种类型的健康数据，为医疗保健专业人员提供了一个全面的了解患者健康的视角，从而能够提供更加个性化和有效的护理。本文回顾了 PCKGs 所涉及的方法、挑战和机遇，重点关注了它们在整合不同 healthcare data 和通过统一健康视角提高护理效果的作用。此外，本文还讨论了 PCKG 开发中的复杂性，包括本体设计、数据集成技术、知识提取和知识结构化表示。它突出了构建和评估 PCKGs 为行动able healthcare 见解所必需的高级技术，如推理、语义搜索和推理机制。我们进一步探讨了 PCKGs 在个性化医学领域的实际应用，强调了它们在改善疾病预测和制定有效治疗计划中的重要性。总之，本文为 PCKGs 在当前技术和最佳实践的状态提供了基础性视角，为这个动态领域未来的研究和应用提供了指导。
URL
https://arxiv.org/abs/2402.12608
